class_docstr: Extension of Transresnet to incorporate dialogue history and multimodality.
docstr: Get the multimodal representation of the encodings.
  
  :param encodings:
      list of encodings
  :param batchsize:
      size of batch
  
  :return:
      final multimodal representations
docstr_tok: ["Get","the","multimodal","representation","of","the","encodings",".",":","param","encodings",":","list","of","encodings",":","param","batchsize",":","size","of","batch",":","return",":","final","multimodal","representations"]
code: def get_rep(self, encodings, batchsize=None):
      """
      Get the multimodal representation of the encodings.
  
      :param encodings:
          list of encodings
      :param batchsize:
          size of batch
  
      :return:
          final multimodal representations
      """
      if not self.multimodal:
          rep = self.sum_encodings(encodings)
      else:
          if self.multimodal_combo == "sum":
              encodings = self.sum_encodings(encodings).unsqueeze(1)
          elif self.multimodal_combo == "concat":
              encodings = self.cat_encodings(encodings)
          all_one_mask = torch.ones(encodings.size()[:2])
          if self.use_cuda:
              all_one_mask = all_one_mask.cuda()
          rep = self.multimodal_encoder(encodings, all_one_mask)
      if rep is None:
          rep = torch.stack(
              [self.blank_encoding for _ in range(batchsize)]
          )
      return rep
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  import torch
  from torch import nn
  from parlai.utils.io import PathManager
  from parlai.agents.transformer.modules import TransformerEncoder, create_position_codes, TransformerEncoderLayer
  from projects.personality_captions.transresnet.modules import TransresnetModel, load_fasttext_embeddings
label_desc: Use image node_id 9 for calling the TransresnetMultimodalModel obj's underlying member method code with example usage: obj.get_rep(encodings, batchsize) and returns: rep